Computation of Interface Aesthetics

People prefer attractive interfaces. Designers strive to outmatch competitors, and create apps and websites that stand out. However, significant expenses on design are unaffordable to small companies; instead, they could adopt automatic tools of interface aesthetics evaluation, a cheaper strategy to good design. This paper describes an important step towards such a tool; it presents eight automatic metrics of graphical user interface (GUI) aesthetics. We tested the metrics in two exploratory studies -- on desktop webpages (N = 62) and on iPhone apps (N = 53) -- and found them to function on both GUI types and for both immediate (150ms exposure) and deliberate (4s exposure) aesthetics impressions. Our best-fit regression models explained up to 49% of variance in webpage aesthetics and up to 32% (if app genre is considered) of variance in iPhone app aesthetics. These results confirm past results and suggest the metrics are valid and reliable enough to be widely discussed, and possibly, to be embedded in our prospective GUI evaluation tool, tLight.